September 2017

Who and where data scientists and machine learning developers are

What background, education and motivations data scientists have and which communities, events and other channels they use to stay up to date

Understanding who data scientists are is key to designing effective campaign messages and offerings. To deliver the message though you also need to know where to find them. In the second part of our report we dive deep into the sources of information that data scientists and ML developers prefer, including among others Q&A sites, communities and conferences.

There has been considerable buzz around data scientists in the last few years. Some argue about how a data scientist is defined, many rush to master the top-X algorithms or skills in order to become one and harvest the advertised lucrative returns, while others appear sceptical about the future need for data scientists given the increasing ability of machines to train each other and improve.

In the 12th edition of our Developer Economics surveys more than 9,700 data scientists and machine learning developers shared their experiences with us. This report is based on their responses and is split in two parts.

In the first part we address the confusion around who data scientists and machine learning developers are, unveiling their background before getting into the field, the education they have had in data science, their level of experience and also why they got involved.We also paint their demographic profile.

Understanding who data scientists are is key to designing effective campaign messages and offerings. To deliver the message though you also need to know where to find them. In the second part of our report we dive deep into the sources of information that data scientists and ML developers prefer, including among others Q&A sites, communities and conferences.

The Who and where data scientists and machine learning developers are report comes with a single user license. If you are interested in an enterprise-wide license please contact us.

In which regions have data science and machine learning penetrated the most?

Are data scientists younger on average than other developers and are women involved in machine learning?

What background did developers have before getting into machine learning and why did they decide to get involved?

How do data scientists get educated in machine learning and data science?

What sources do data scientists go to in order to find information?

Which Q&A sites do users of different frameworks prefer?

Which communities do experienced machine learning developers prefer as compared to beginners?

To get a full list of this report’s contents and a sample graph, please download the brochure.

This report is based on the 12th Developer Economics survey, a large-scale online developer survey designed, produced and carried out by SlashData. They survey received 9,714 responses from data scientists and machine learning developers. Their answers formed the basis of the insights of this report.

The online survey was translated in 7 languages (simplified Chinese, Japanese, Korean, Portuguese, Russian, Spanish, and Vietnamese) and promoted by 82 leading community and media partners within the software development industry. We corrected for regional bias and segment distribution bias across our outreach channels.